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arXiv Machine Learning

A2QTGN: Adaptive Amplitude Quantum-Integrated Temporal Graph Network for Dynamic Link Prediction

arXiv:2605. 21916v2 Announce Type: replace-cross Abstract: Dynamic link prediction is important for modeling evolving interactions in social, communication, financial, and transportation networks.

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arXiv Machine Learning
Sep 18

Quantum Graph Convolutional Networks: Implementation and Trainability Analysis

The paper implements two quantum graph neural network architectures—Simplified Graph Convolution (SGC) and Linear Graph Convolution (LGC)—and evaluates them on benchmark graph datasets for semi‑supervised learning using quantum simulation. It compares their predictive performance and optimization behavior to classical baselines, finding that the quantum models achieve competitive results with fewer parameters. Additionally, the study provides a cost‑gradient analysis to identify trainable tasks and a classical simulability investigation to determine regimes where the circuits remain robust during training.

By Paul San Sebastian Sein, Theodor Iosif, Tilen G. Limb\"ack-Stokin, Kin Ian Lo, Yidong Liao
benchmarks
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arXiv Machine Learning
Jul 23

Edge-Local and Qubit-Efficient Quantum Graph Learning for the NISQ Era

arXiv:2602. 16018v2 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) are a powerful framework for learning representations from graph-structured data, but their direct implementation on near-term quantum hardware remains challenging due to circuit depth, multi-qubit interactions, and qubit scalability constraints.

By Armin Ahmadkhaniha, Jake Doliskani
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arXiv Machine Learning
1d ago

Overcoming the Representational Limitations of Amplitude Encoding for Quantum Neural Networks

The paper identifies a representational limitation of amplitude encoding in quantum neural networks: normalization makes the encoding invariant to nonzero real rescaling of inputs, which restricts the hypothesis class regardless of the circuit architecture. The authors derive a condition for deterministic preprocessing that removes this ambiguity and propose a fixed nonlinear feature map that distinguishes all real inputs while keeping logarithmic qubit scaling. Experiments on three classification benchmarks and a synthetic dataset show that this new encoding improves accuracy by 21–44 % over both conventional amplitude and angle encodings and a classical neural network.

By Vincent Gurgul, David Prinz
benchmarks
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arXiv Machine Learning
Oct 9

Large-Scale Benchmarking of Quantum Neural Network Configurations for Financial Time Series Forecasting

The paper reports a large‑scale systematic comparison of 1,368 quantum neural network (QNN) configurations for forecasting the GBP/USD spot exchange rate. By varying encoding methods, ansatz designs, qubit counts, layer depths, and cost functions, the study finds that gate selection and arrangement are more critical than parameter count, and that entanglement is a system‑level property. The top QNN achieves an R² of 0.985, surpassing a classical BiLSTM, while tests on the IQM Emerald device show gate errors and decoherence as major obstacles, with gate choice and circuit depth determining noise resilience.

By Jack Waller, Xing Liang, Dimitrios Makris, Rajagopal Nilavalan
benchmarks
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arXiv AI
Jul 9

QCNN with Rough Path Signature Kernels

arXiv:2607. 07634v1 Announce Type: cross Abstract: Time series analysis plays a vital role across a wide range of scientific and engineering domains but poses substantial computational challenges.

By Leonardo Nogueira Falabella, Vasily Sazonov
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